Understanding the process of drug repurposing is critically significant for drug development. In this paper, we employ extracted bio-entities to detect the features of different phases in drug repurposing. We proposed a transparent and easy entitymetric indicator for bio-entities, i.e., Popularity Index, to quantify and visualize the dynamic changes in academic interests of bio-entities. By taking aspirin as an example, the results display specific profiles of drug repurposing and the evolution of bio-entities in the different phases of drug research, which would potentially be valuable for pharmaceutical companies and scholars to successfully discover and repurpose drugs.The increasing availability of electronic health record data offers unprecedented opportunities for predictive modeling in healthcare informatics including outcomes such as mortality and disease diagnosis as well as risk factor identification. Recently, deep neural networks (DNNs) have been successfully applied in healthcare informatics and achieved state-of-art predictive performance. However, existing DNN models either rely on the pre-defined patient subgroups or take the "one-size-fits-all" approach and are built without considering patient stratification. Consequently, those models are not able to discover patient subgroups and the risk factors are thereafter identified for the entire patient population, failing to account for potential group differences. To address this challenge, we propose the use of deep mixture neural networks (DMNN), a unified DNN model for simultaneous patient stratification and predictive modeling. Experimental results on a clinic dataset show that our proposed DMNN can achieve good performance on predicting diagnosis of acute heart failure. With DMNN's ability to incorporate patient stratification, we are able to systematically identify group-specific risk factors for different patient subgroups which could potentially shed light on revealing factors that contribute to outcome differences.Diagnostic errors can pose a serious threat to patient safety, leading to serious harm and even death. Efforts are being made to develop interventions that allow physicians to reassess for errors and improve diagnostic accuracy. Our study presents an exploration of misdiagnosis patterns mined from PubMed abstracts. Article titles containing certain phrases indicating misdiagnosis were selected and frequencies of these misdiagnoses calculated. We present the resulting patterns in the form of a directed graph with frequency-weighted misdiagnosis edges connecting diagnosis vertices. We find that the most commonly misdiagnosed diseases were often misdiagnosed as many different diseases, with each misdiagnosis having a relatively low frequency, rather than as a single disease with greater probability. Additionally, while a misdiagnosis relationship may generally exist, the relationship was often found to be one-sided.Timely accrual continues to be a challenge in clinical trials. The evolution of Electronic Health Record systems and cohort selection tools like i2b2 have improved identification of potential candidate participants. However, delays in receiving relevant patient information and lack of real time patient identification cause difficulty in meeting recruitment targets. The authors have designed and developed a proof of concept platform that informs authorized study team members about potential participant matches while the patient is at a healthcare setting. This Just-In-Time Alert (JITA) application leverages Health Level 7 (HL7) messages and parses them against study eligibility criteria using Amazon Web Services (AWS) cloud technologies. https://www.selleckchem.com/products/elenbecestat.html When required conditions are satisfied, the rules engine triggers an alert to the study team. Our pilot tests using difficult to recruit trials currently underway at the UMass Medical School have shown significant potential by generating more than 90 patient alerts in a 90-day testing timeframe.Adverse events (AEs) are undesirable outcomes of medication administration and cause many hospitalizations as well as even deaths per year. Information about AEs can enable their prevention. Natural language processing (NLP) techniques can identify AEs from narratives and match them to a structured terminology. We propose a novel neural network for AE normalization utilizing bidirectional long short-term memory (biLSTM) with attention mechanism that generalizes to diverse datasets. We train this network to first learn a framework for general AE normalization and then to learn the specifics of the task on individual corpora. Our results on the datasets from the Text Analysis Conference (TAC) 2017-ADR track, FDA adverse drug event evaluation shared task, and the Social Media Mining for Health Applications Workshop &amp; Shared Task 2019 show that our approach outperforms widely used rule-based normalizers on a diverse set of narratives. Additionally, it outperforms the best normalization system by 4.86 in macro-averaged F1-score in the TAC 2017-ADR track.Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach based on deep learning to automatically identify clinically important recommendations in radiology reports. Our approach first identifies the recommendation sentences and then extracts reason, test, and time frame of the identified recommendations. To train our extraction models, we created a corpus of 1367 radiology reports annotated for recommendation information. Our extraction models achieved 0.93 f-score for recommendation sentence, 0.65 f-score for reason, 0.73 f-score for test, and 0.84 f-score for time frame. We applied the extraction models to a set of over 3.3 million radiology reports and analyzed the adherence of follow-up recommendations.Electronic health records (EHRs) provide a wealth of data for phenotype development in population health studies, and researchers invest considerable time to curate data elements and validate disease definitions. The ability to reproduce well-defined phenotypes increases data quality, comparability of results and expedites research. In this paper, we present a standardized approach to organize and capture phenotype definitions, resulting in the creation of an open, online repository of phenotypes. This resource captures phenotype development, provenance and process from the Million Veteran Program, a national mega-biobank embedded in the Veterans Health Administration (VHA). To ensure that the repository is searchable, extendable, and sustainable, it is necessary to develop both a proper digital catalog architecture and underlying metadata infrastructure to enable effective management of the data fields required to define each phenotype. Our methods provide a resource for VHA investigators and a roadmap for researchers interested in standardizing their phenotype definitions to increase portability.